Users currently use a wide variety of computing devices to participate in a networked marketplace. Users typically search for a wide variety of items that may or may not be available at the networked marketplace.
In some examples, users may search for very generic terms hoping to find what they are looking for. For example, a user may desire to purchase poker chips and may search for “chips.” Because the networked marketplace may include many different kinds or types of “chips,” the marketplace may not be able to determine what the user is looking for.
In other examples, a system may present a variety of products that include the term “chips,” and may remember items users have clicked. The system may infer that future users are likely looking for similar items. However, because the system may not present the user with a complete spectrum of products based on the search term, the system may suffer from a presentation bias. Therefore, a system developed in this way may still not be able to increase accuracy of search results.
In another example, a system may relate sales with queries. However, because sales represent a very small data sample as compared with item views or selections, the system may not acquire sufficient data to learn what users are likely looking for.
Furthermore, a wide variety of different users may use the networked marketplace and may commonly understand terms to mean distinct things, or may use generic terms intending to purchase different things. Therefore, accommodating a wide variety of very different users may be difficult.